Daniel Fraiman and Ricardo Fraiman
The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.
César Rennó-Costa, Ana Cláudia Costa da Silva, Wilfredo Blanco and Sidarta Ribeiro
Aline Duarte, Antonio Galves, Eva Löcherbach and Guilherme Ost
Claudia Vargas, Fernanda Torres, Bia Ramalho, Cristiane Patroclo, Lidiane Souza, Fernanda Guimarães, José Vicente Martins and Maria Luíza Rangel
Amorim MR, Pena RFO, Souza GMPR, Bonagamba LGH, Roque AC and Machado BH
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